Dendritic Cell Algorithm Signal Weighting for Malware Detection
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Solution Overview
Problem
Existing Dendritic Cell Algorithm (DCA) implementations in malware detection often ignore strong signals due to being drowned out by a large number of nominal signals, as they typically use a single signal vector and average or median multiple indicator outputs, failing to effectively analyze the system's state with multiple feature indicators.
Innovation Solution
The system combines and weights multiple signal vectors from various indicators, sorting them by type and magnitude, applying an exponentially increasing decay factor to prioritize strong signals while minimizing the impact of nominal signals, thereby creating a combined single signal vector for accurate threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple indicator outputs are combined using average or median, then the system considers all signals, but strong signals are drowned out by nominal signals
Solution Approach 1:
The patent transforms the combination method from simple averaging/median to a weighted sum approach where weights are determined by signal strength and decay factors. This parameter change in the combination function allows strong signals to dominate while still incorporating nominal signals, resolving the contradiction between considering all signals and maintaining detection accuracy
Solution Approach 2:
The patent introduces asymmetric weighting where different decay factors are applied based on signal type and strength. Strong PAMP signals receive different treatment compared to nominal danger signals, creating an asymmetric combination that prevents strong signals from being drowned out while maintaining comprehensive signal consideration
2Device complexity
If a single signal vector is used, then the system is simple to implement, but it cannot effectively analyze multiple feature indicators
Solution Approach 1:
The patent segments the signal processing into distinct stages: individual indicator evaluation, signal vector generation with four components, weighted combination of multiple vectors, and final state determination. This segmentation allows complex multi-indicator analysis while maintaining manageable implementation complexity through modular processing
Solution Approach 2:
The patent extends the signal representation from a single scalar value to a four-dimensional signal vector (PAMP, danger, safe, inflammation). This dimensional expansion enables rich multi-feature indicator analysis while the structured vector approach keeps implementation complexity manageable through organized data representation
3Measurement precision
If decay factors are applied to weight signals, then strong signals are prioritized, but the impact of nominal signals is minimized
Solution Approach 1:
The patent applies partial weighting through decay factors where strong signals receive minimal decay (preserving their impact) while nominal signals receive greater decay (reducing their impact). This partial action approach maintains detection accuracy by emphasizing strong signals while still incorporating nominal signals to avoid complete information loss
Solution Approach 2:
The patent dynamically adjusts the decay factor parameter based on signal characteristics and position in the sorted list. This parameter change strategy allows flexible control over the balance between prioritizing strong signals and preserving information from nominal signals, optimizing both detection accuracy and information retention
Data Source
AI summary
Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. A DCA module may receive an output or signal from multiple indicators concerning the state of at least a portion of the system. The DCA module is configured to combine the plurality of signals into a single signal vector. The DCA module may be configured to sort the received signals based on signal type and magnitude of each signal. The DCA module may then use a decay factor to weight the received signals so that a large number of “nominal” signals do not drown out a small number of “strong” signals indicating a malware attack. The decay factor may be exponentially increased each time it is applied so that all received signals are considered by the DCA module, but so that the “nominal” signals may have a minimal effect.